OBJECTIVE:Based on different algorithms of machine learning,the prediction model of lumbar disc herniation has become a trend and hot spot in the development of precision medicine.However,there is limited evidence on the reporting quality and methodological quality of prediction models of lumbar disc herniation outcomes using machine learning.This article is aimed to explore the performance of machine learning algorithms in predicting the prognosis of lumbar disc herniation by comprehensively analyzing the report quality and risk of bias of previous studies that developed and validated prognosis prediction models based on machine learning through a comprehensive literature search,in order to explore the performance of machine learning algorithms in predicting the prognosis of lumbar disc herniation.METHODS:The databases of CNKI,WanFang,VIP,SinOMED,PubMed,Web of Science,Embase,and The Cochrane Library were searched by computer.Studies on the use of machine learning to develop(and/or validate)prognostic prediction models for lumbar disc herniation were collected from the inception of the database to December 31,2023.Two researchers independently screened the literature,extracted data,and assessed the risk of bias of the included studies.The reporting quality and risk of bias of the included studies were assessed by the Multivariable Transparent Reporting of Predictive Models(TRIPOD)statement and the Predictive Model Risk of Bias Assessment Tool(PROBAST).The results of the evaluation were analyzed using descriptive statistics and visual charts.RESULTS:(1)A total of 23 articles were included,and the TRIPOD compliance of each study ranged from 11％to 87％,with a median compliance of 54％.The quality of reporting of titles,detailed descriptions of treatment measures,blinding of predictors,handling of missing data,details of risk stratification,specific procedures for enrollment,model interpretation,and model performance was mostly poor,with TRIPOD adherence rates ranging from 4％to 35％.(2)Of all included studies,61％had a high risk of bias and 39％had an unclear overall risk of bias.The area under the curve,accuracy,sensitivity and specificity were used to evaluate the performance of the model.The areas under the curve of 20 models were reported,ranging from 0.561 to 0.999.Three models reported the accuracy of the model,ranging from 82.07％to 89.65％.(3)Among all included studies,the statistical analysis domain was most often assessed as having a high risk of bias,mainly due to the small number of valid samples,the selection of predictors based on univariate analysis and the lack of calibration and discrimination assessment of the model in the study.CONCLUSION:These results indicate that machine learning can achieve good predictive ability in the development and validation of prognostic models for lumbar disc herniation.The commonly used algorithms include regression algorithm,support vector machine,decision tree,random forest,artificial neural network,naive Bayes and other algorithms.Reasonable algorithms combined with clinical practice can improve the accuracy of prognosis prediction of lumbar disc herniation.However,the reporting and methodological quality of prognosis prediction models based on machine learning are poor,the prediction performance of different models varies greatly,and the generalization and extrapolation of research models are unclear.There is an urgent need to improve the design,implementation and reporting of such studies.To promote the application of machine learning in the clinical practice of lumbar disc herniation prediction models,it is necessary to comprehensively consider various predictors related to the prognosis of the disease before modeling,and strictly follow the relevant standards of PROBAST tool during modeling.

OBJECTIVE:Based on different algorithms of machine learning,the prediction model of lumbar disc herniation has become a trend and hot spot in the development of precision medicine.However,there is limited evidence on the reporting quality and methodological quality of prediction models of lumbar disc herniation outcomes using machine learning.This article is aimed to explore the performance of machine learning algorithms in predicting the prognosis of lumbar disc herniation by comprehensively analyzing the report quality and risk of bias of previous studies that developed and validated prognosis prediction models based on machine learning through a comprehensive literature search,in order to explore the performance of machine learning algorithms in predicting the prognosis of lumbar disc herniation.METHODS:The databases of CNKI,WanFang,VIP,SinOMED,PubMed,Web of Science,Embase,and The Cochrane Library were searched by computer.Studies on the use of machine learning to develop(and/or validate)prognostic prediction models for lumbar disc herniation were collected from the inception of the database to December 31,2023.Two researchers independently screened the literature,extracted data,and assessed the risk of bias of the included studies.The reporting quality and risk of bias of the included studies were assessed by the Multivariable Transparent Reporting of Predictive Models(TRIPOD)statement and the Predictive Model Risk of Bias Assessment Tool(PROBAST).The results of the evaluation were analyzed using descriptive statistics and visual charts.RESULTS:(1)A total of 23 articles were included,and the TRIPOD compliance of each study ranged from 11％to 87％,with a median compliance of 54％.The quality of reporting of titles,detailed descriptions of treatment measures,blinding of predictors,handling of missing data,details of risk stratification,specific procedures for enrollment,model interpretation,and model performance was mostly poor,with TRIPOD adherence rates ranging from 4％to 35％.(2)Of all included studies,61％had a high risk of bias and 39％had an unclear overall risk of bias.The area under the curve,accuracy,sensitivity and specificity were used to evaluate the performance of the model.The areas under the curve of 20 models were reported,ranging from 0.561 to 0.999.Three models reported the accuracy of the model,ranging from 82.07％to 89.65％.(3)Among all included studies,the statistical analysis domain was most often assessed as having a high risk of bias,mainly due to the small number of valid samples,the selection of predictors based on univariate analysis and the lack of calibration and discrimination assessment of the model in the study.CONCLUSION:These results indicate that machine learning can achieve good predictive ability in the development and validation of prognostic models for lumbar disc herniation.The commonly used algorithms include regression algorithm,support vector machine,decision tree,random forest,artificial neural network,naive Bayes and other algorithms.Reasonable algorithms combined with clinical practice can improve the accuracy of prognosis prediction of lumbar disc herniation.However,the reporting and methodological quality of prognosis prediction models based on machine learning are poor,the prediction performance of different models varies greatly,and the generalization and extrapolation of research models are unclear.There is an urgent need to improve the design,implementation and reporting of such studies.To promote the application of machine learning in the clinical practice of lumbar disc herniation prediction models,it is necessary to comprehensively consider various predictors related to the prognosis of the disease before modeling,and strictly follow the relevant standards of PROBAST tool during modeling.

The surgical management of empyema (excluding those caused by mycobacterium tuberculosis and non-tuberculous mycobacteria) is rapidly evolving towards minimally invasive, precise, and stepwise approaches. The traditional three-stage classification (exudative, fibrinopurulent, and organizing) has limitations in guiding dynamic clinical decision-making. For the first time, this consensus explicitly identifies two critical junctures in the pathological progression of empyema: "early transformation" (stage Ⅰ to Ⅱ) and "late transformation" (stage Ⅱ to Ⅲ), and thereby constructs a corresponding "identification-early warning-intervention" stepwise therapeutic framework. The consensus emphasizes that proactive debridement via video-assisted thoracoscopic surgery should be performed during the early transformation phase to halt disease progression. Conversely, during the late transformation phase, therapeutic goals should be rationally adjusted to prioritize adequate drainage, avoiding futile pleural decortication. Moreover, the consensus underscores the pivotal role of precise perioperative etiological diagnosis (e.g. metagenomic nest-generation sequencing) and standardized anti-infective therapy. Integrating practical experiences from multiple thoracic surgery centers in China and relevant evidence-based literature, this consensus formulates recommendations on the precise definitions of staging, surgical indications for each phase, key technical points, perioperative management, and training systems. It aims to promote the standardized and individualized surgical management of empyema, ultimately optimizing patient prognosis.

Ursodeoxycholic acid (UDCA) is a naturally occurring, low-toxicity, and hydrophilic bile acid (BA) in the human body that is converted by intestinal flora using primary BA. Solute carrier family 7 member 11 (SLC7A11) functions to uptake extracellular cystine in exchange for glutamate, and is highly expressed in a variety of human cancers. Retroperitoneal liposarcoma (RLPS) refers to liposarcoma originating from the retroperitoneal area. Lipidomics analysis revealed that UDCA was one of the most significantly downregulated metabolites in sera of RLPS patients compared with healthy subjects. The augmentation of UDCA concentration (≥25 μg/mL) demonstrated a suppressive effect on the proliferation of liposarcoma cells. [¹⁵N₂]-cystine and [¹³C₅]-glutamine isotope tracing revealed that UDCA impairs cystine uptake and glutathione (GSH) synthesis. Mechanistically, UDCA binds to the cystine transporter SLC7A11 to inhibit cystine uptake and impair GSH de novo synthesis, leading to reactive oxygen species (ROS) accumulation and mitochondrial oxidative damage. Furthermore, UDCA can promote the anti-cancer effects of ferroptosis inducers (Erastin, RSL3), the murine double minute 2 (MDM2) inhibitors (Nutlin 3a, RG7112), cyclin dependent kinase 4 (CDK4) inhibitor (Abemaciclib), and glutaminase inhibitor (CB839). Together, UDCA functions as a cystine exchange factor that binds to SLC7A11 for antitumor activity, and SLC7A11 is not only a new transporter for BA but also a clinically applicable target for UDCA. More importantly, in combination with other antitumor chemotherapy or physiotherapy treatments, UDCA may provide effective and promising treatment strategies for RLPS or other types of tumors in a ROS-dependent manner.

In recent years, hybrid brain-computer interfaces (BCIs) have gained significant attention due to their demonstrated advantages in increasing the number of targets and enhancing robustness of the systems. However, Existing studies usually construct BCI systems using intense auditory stimulation and strong central visual stimulation, which lead to poor user experience and indicate a need for improving system comfort. Studies have proved that the use of peripheral visual stimulation and lower intensity of auditory stimulation can effectively boost the user's comfort. Therefore, this study used high-frequency peripheral visual stimulation and 40-dB weak auditory stimulation to elicit steady-state visual evoked potential (SSVEP) and auditory steady-state response (ASSR) signals, building a high-comfort hybrid BCI based on weak audio-visual evoked responses. This system coded 40 targets via 20 high-frequency visual stimulation frequencies and two auditory stimulation frequencies, improving the coding efficiency of BCI systems. Results showed that the hybrid system's averaged classification accuracy was (78.00 ± 12.18) %, and the information transfer rate (ITR) could reached 27.47 bits/min. This study offers new ideas for the design of hybrid BCI paradigm based on imperceptible stimulation.
Brain-Computer Interfaces ; Humans ; Evoked Potentials, Visual/physiology* ; Acoustic Stimulation ; Photic Stimulation ; Electroencephalography ; Evoked Potentials, Auditory/physiology* ; Adult

OBJECTIVE: To investigate the analgesic effect of locally injecting a "cocktail" analgesia containing a high-dose compound betamethasone during revision hip arthroplasty, and also to study the usage of opioid drugs. METHODS: A retrospective analysis was conducted on the clinical data of 180 patients who underwent revision hip arthroplasty due to aseptic loosening of the hip prosthesis between January 2015 and December 2021. Among them, 95 patients received intraoperative injection of "cocktail" analgesia containing high-dose compound betamethasone (group A), and 85 patients received intraoperative injection of traditional "cocktail" analgesia (group B). There was no significant difference in baseline data such as gender, age, body mass index, presence or absence of diabetes mellitus between the two groups ( P>0.05). The hospital stay, use of opioid drugs within 72 hours, and the incidence of adverse reactions within 72 hours after operation [including nausea and vomiting, insomnia, deep venous thrombosis (DVT), infection, etc.] were recorded and compared between the two groups. The pain relief of patients was evaluated using the static and dynamic visual analogue scale (VAS) scores at 12, 24, 48, and 72 hours after operation. The incidence of complications (including prosthesis re-loosening, hip joint dislocation, hip joint stiffness, limping, chronic pain, etc.) at 2 years after operation was recorded, and the Harris Hip Score (HHS) was used to evaluate the function at 2 years after operation. RESULTS: In group A, the utilization rate of opioid drugs within 72 hours after operation was significantly lower than that in group B ( P<0.05). However, there was no significant difference between the two groups in terms of hospital stay, as well as the incidence of adverse reactions such as nausea and vomiting, insomnia, DVT, and infection within 72 hours after operation ( P>0.05). The VAS scores of both groups decreased with time, and the differences between different time points were significant ( P<0.05). The static and dynamic VAS scores of group A were significantly lower than those of group B at 12, 24, and 48 hours after operation ( P<0.05), but there was no significant difference in static and dynamic VAS scores between the two groups at 72 hours after operation ( P>0.05). All patients in both groups were followed up 2-8 years, with an average of 5.73 years. At 2 years after operation, no significant difference was found between the two groups in the incidence of complications and HHS score ( P>0.05). CONCLUSION "Cocktail" analgesia containing a high-dose compound betamethasone for early analgesia after revision hip arthroplasty can effectively reduce postoperative pain and the use of opioid drugs, but will not increase the incidence of infection and DVT after operation.
Humans ; Arthroplasty, Replacement, Hip/adverse effects* ; Betamethasone/therapeutic use* ; Retrospective Studies ; Male ; Female ; Analgesics, Opioid/administration & dosage* ; Pain, Postoperative/prevention & control* ; Middle Aged ; Reoperation ; Aged ; Analgesia/methods* ; Adult ; Pain Measurement ; Pain Management/methods* ; Prosthesis Failure ; Hip Prosthesis

Objective:To evaluate the treatment efficacy of prostatic abscess and summarize clinical experience.Methods:A retrospective analysis was conducted on clinical data from 23 patients diagnosed with prostatic abscess at Yanbian University Hospital between December 2019 and April 2024. The mean age was (57.72±16.09) years, and BMI was (22.63±2.80) kg/m 2. Comorbidities included diabetes mellitus in 15 cases (65.2%), cardiovascular/cerebrovascular diseases in 12 cases, and hepatitis with cirrhosis in 2 cases. Medical histories comprised benign prostatic hyperplasia in 3 cases (13.0%), prostatitis in 3 cases (13.0%), and recent catheterization in 7 cases. Clinical manifestations included lower urinary tract symptoms in 19 cases and varying degrees of fever in 14 cases. All patients underwent urinary bacterial culture upon admission, with positive results in 8 cases (34.8%): Klebsiella pneumoniae (4 cases), Escherichia coli (2 cases), Staphylococcus aureus (1 case), and Pseudomonas aeruginosa (1 case). All patients received urinary tract ultrasound, while 10 underwent full abdominal CT and 20 underwent prostate MRI. Prostatic abscess was confirmed in 20 cases, while 3 suspected cases were intraoperatively confirmed. In the 23 cases, the maximum abscess cavity diameter ranged from 11 to 70 mm; multiple abscesses (≥2) were observed in 12 cases (52.2%), with pelvic and scrotal involvement noted in 4 cases. All patients received systemic antibiotic therapy, with 4 cases managed conservatively due to favorable baseline status, significant response to antibiotics, and low abscess complexity; 4 cases with favorable baseline status, good response to anti-infective therapy, and younger age, along with at least one large abscess cavity (33-70 mm in diameter), underwent ultrasound-guided transperineal drainage after confirmation of abscess localization strictly within the prostatic region and established purulent material; and 15 cases with poorly controlled infections and complex abscesses (multiloculated/multiple abscesses/extensive involvement/diameter ≥30 mm/volume ≥20 cm 2), abscess cavity (11-40 mm in diameter), underwent transurethral incision and drainage. Results:The follow-up period ranged from 3 months to 1 year, with a mean duration of 10.8 months. No residual or recurrent abscesses were observed in the transurethral incision and drainage group, while 2 cases in the ultrasound-guided drainage group and 1 case in the conservative management group showed persistent abscesses, all of whom were diabetic patients. A comparative analysis stratified by diabetes status revealed significantly higher levels of white blood cell count [16.7 (11.8, 17.4) ×10 9/L vs. 11.9 (7.1, 14.5) ×10 9/L, P = 0.045] and procalcitonin [1.40 (0.8, 3.2) ng/ml vs. 0.3(0, 2.0) ng/ml, P = 0.042] at admission in the diabetic group compared to the non-diabetic group. Conclusions:If the patient has a favorable baseline status and demonstrates a stable clinical response to antibiotic therapy, either ultrasound-guided drainage or conservative management may be selected based on abscess characteristics such as size and location. However, for patients with complex abscesses, particularly those showing suboptimal response to antibiotics, transurethral incision and drainage is recommended. This study also identified diabetes mellitus as a potential significant factor influencing both the severity at disease onset and the prognosis of prostatic abscess.

Objective:Autoimmune gastritis (AIG) is characterized by the loss of acid-secreting glands, resulting in hypochlorhydria and hypergastrinemia, conditions that significantly increase the risk of developing gastric neuroendocrine tumors (NETs) and gastric adenocarcinoma. In recent years, AIG has garnered increasing attention in both clinical and research settings. However, comprehensive studies on the clinical and endoscopic characteristics of AIG particularly cases complicated by gastric neoplastic lesions remain limited in China. This study aims to comprehensively summarize the clinical and endoscopic features of AIG and its associated gastric neoplastic lesions.Methods:A retrospective analysis was conducted using medical records from patients with AIG diagnosed at Sichuan Provincial People′s Hospital between 2019 and 2024. Data collected included demographic information, medical history, serological test results, imaging findings, and endoscopic observations. The clinical and endoscopic features of AIG patients with gastric NETs or epithelial-derived tumors were compared to those without gastric neoplastic lesions to identify potential risk factors and diagnostic indicators for tumor development in AIG.Results:A total of 72 patients with AIG were included, of whom 62.5% (45/72) were female, with an age range of 30 to 79 years old (mean age: 57±11 years). Parietal cell antibody (PCA) positivity was observed in 93.1% (67/72), intrinsic factor antibody (IFA) positivity in 45.8% (33/72), and Helicobacter pylori ( H. pylori) co-infection in 48.6% (35/72). Endoscopically, 84.7% (61/72) showed prominent corpus-dominant advanced atrophy; 47.2% (34/72) had sticky adherent mucus; and 41.7% (30/72) displayed residual oxyntic mucosa in the gastric body or fundus. Only 23.6% (17/72) had normal antrum mucosa, and just 16.7% (12/72) showed a circular wrinkle-like pattern. Gastric neoplastic lesions were identified in 35 patients (48.6%), including 15 cases (20.8%) with NETs and 20 cases (27.8%) with epithelial-derived tumors (four adenocarcinomas, three adenomas, and 13 cases of intraepithelial neoplasia). No significant differences were found between tumor and non-tumor groups in terms of age, gender, PCA/IFA positivity, gastrin levels, anemia status, folic acid, or serum iron levels. However, patients with NETs had significantly lower vitamin B 12 levels compared to those without tumors (183±111 ng/L vs. 323±159 ng/L, t=2.47, P=0.042). Additionally, AIG patients with NETs were more likely to be H.pylori-negative compared to both the non-tumor group (66.7% vs. 35.1%, χ2=5.26, P=0.072) and the epithelial-derived tumor group (66.7% vs. 30.0%, χ2=5.80, P=0.055). The incidence of reverse atrophy in the epithelial-derived tumor group was significantly lower than that in the non-tumor group (65.0% vs. 91.9%, χ2=6.49, P=0.011) and the NETs group (65.0% vs. 93.3%, χ2=3.90, P=0.048).? Conclusion:In AIG patients with NETs, serum vitamin B 12 levels are significantly reduced, suggesting that vitamin B 12 deficiency may be a key risk factor or clinical indicator for NET development in AIG. Furthermore, NETs are more frequently observed in AIG patients without H.pylori infection, while epithelial-derived tumors are more commonly associated with H.pylori co-infection.

Objective To investigate the changes in the levels of D-dimer(D-D),fibrin degradation product(FDP),thrombomodulin(TM),thrombin-antithrombin complex(TAT),tissue plasminogen activator-plasminogen activator inhibitor-1 complex(t-PAIC),and plasmin-alpha 2 plasmin inhibitor complex(PIC)during the treatment of patients with acute myeloid leukemia(AML)and their predictive value for efficacy and prognosis.Methods Thirty-one AML patients initially diagnosed at Guidong People's Hospital of Guangxi Zhuang Autonomous Region from January 2022 to December 2024 were collected as observation group,and 34 healthy medical checkupers were selected as control group.The changes in the levels of the indicators between two groups as well as before,during and after the treatment of observation group were compared,and observation group was divided into remission group and non-remission group according to the efficacy criteria,and the levels of the indicators between two groups were further compared.Risk factors for the efficacy and prognosis of AML patients were analyzed by using Logistic regression analysis.Results Before treatment,the levels of prothrombin time(PT),activated partial thromboplastin time(APTT),D-D,FDP,t-PAIC,PIC,TM and TAT in observation group were significantly higher than those in control group(P＜0.05),whereas there was no significant difference in fibrinogen(Fib)compared with control group(P＞0.05).During the treatment,the levels of t-PAIC and TM in observation group were higher than those before treatment(P＜0.05);After treatment,the levels of PT,D-D,FDP,PIC and TAT in observation group were significantly lower than those before treatment(P＜0.05).After treatment,the levels of PT,D-D,FDP,t-PAIC,PIC,TM and TAT were all lower than those during treatment(P＜0.05).The D-D,FDP,t-PAIC,PIC,TAT and TM levels in non-remission group were all higher than those in remission group,with the t-PAIC and TM levels showing statistical significance(P＜0.05).Binary Logistic regression analysis showed that t-PAIC was an independent risk factor affecting the efficacy and prognosis of AML patients(P＜0.05,OR=1.205,95％CI:1.015-1.430).Conclusion Regular testing of D-D,FDP,TM,TAT,t-PAIC and PIC levels can help to assess the disease changes and efficacy prognosis of AML patients,and provide an important reference for clinical decision-making.

Ursodeoxycholic acid(UDCA)is a naturally occurring,low-toxicity,and hydrophilic bile acid(BA)in the human body that is converted by intestinal flora using primary BA.Solute carrier family 7 member 11(SLC7A11)functions to uptake extracellular cystine in exchange for glutamate,and is highly expressed in a variety of human cancers.Retroperitoneal liposarcoma(RLPS)refers to liposarcoma originating from the retroperitoneal area.Lipidomics analysis revealed that UDCA was one of the most significantly down-regulated metabolites in sera of RIPS patients compared with healthy subjects.The augmentation of UDCA concentration(≥25 μg/mL)demonstrated a suppressive effect on the proliferation of liposarcoma cells.[15N2]-cystine and[13Cs]-glutamine isotope tracing revealed that UDCA impairs cystine uptake and glutathione(GSH)synthesis.Mechanistically,UDCA binds to the cystine transporter SLC7A11 to inhibit cystine uptake and impair GSH de novo synthesis,leading to reactive oxygen species(ROS)accumulation and mitochondrial oxidative damage.Furthermore,UDCA can promote the anti-cancer effects of ferroptosis inducers(Erastin,RSL3),the murine double minute 2(MDM2)inhibitors(Nutlin 3a,RG7112),cyclin dependent kinase 4(CDK4)inhibitor(Abemaciclib),and glutaminase inhibitor(CB839).Together,UDCA functions as a cystine exchange factor that binds to SLC7A11 for antitumor activity,and SLC7A11 is not only a new transporter for BA but also a clinically applicable target for UDCA.More importantly,in combination with other antitumor chemotherapy or physiotherapy treatments,UDCA may provide effective and promising treatment strategies for RLPS or other types of tumors in a ROS-dependent manner.